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OverviewThis book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods. Full Product DetailsAuthor: Luc Bauwens , Jean-Francois Richard (University Professor of Economics, University of Pittsburgh, USA) , Michel LubranoPublisher: Oxford University Press Imprint: Oxford University Press Dimensions: Width: 15.50cm , Height: 2.00cm , Length: 23.50cm Weight: 0.536kg ISBN: 9780198773139ISBN 10: 0198773137 Pages: 366 Publication Date: 01 December 1999 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsChapter 1: Decision Theory and Bayesian Inference ; Chapter 2: Bayesian Statistics and Linear Regression ; Chapter 3: Methods of Numerical Integration ; Chapter 4: Prior Densities for the Regression Model ; Chapter 5: Dynamic Regression Models ; Chapter 6: Bayesian Unit Roots ; Chapter 7: Heteroskedasticity and ARCH ; Chapter 8: Nonlinear Tome Series Models ; Chapter 9: Systems of Equations ; Appendix A: Probability Distributions ; Appendix B: Generating Random NumbersReviewsit can serve as a useful textbook for advanced undergraduate or graduate courses in either time series analysis or econometrics. Paul Goodwin, International Journal of Forecasting, 2000 presents a comprehensive review of dynamic econometric models from a Bayesian perspective ... four insightful introductory chapters ... provide a valuable synthesis of current ideas and their application to parameter estimation. Paul Goodwin, International Journa of Forecasting, 2000 `it can serve as a useful textbook for advanced undergraduate or graduate courses in either time series analysis or econometrics.' Paul Goodwin, International Journal of Forecasting, 2000 `presents a comprehensive review of dynamic econometric models from a Bayesian perspective ... four insightful introductory chapters ... provide a valuable synthesis of current ideas and their application to parameter estimation.' Paul Goodwin, International Journa of Forecasting, 2000 Author InformationLuc Bauwens is currently Professor of Economics at the Universite catholique de Louvain, where he has been co-director of the Center for Operations Research and Econometrics (CORE) from 1992 to 1998. He has previously been a lecturer at Ecole des Hautes Etudes en Sciences Sociales (EHESS), France, at Facultes universitaires catholiques de Mons (FUCAM), Belgium, and a consultant at the World Bank, Washington DC. His research interests cover Bayesian inference, time series methods, simulation and numerical methods in econometrics, as well as empirical finance and international trade. Michel Lubrano is Directeur de Recherche at CNRS, part of GREQAM in Marseille. Jean-Francois Richard is University Professor of Economics at the University of Pittsburgh. Tab Content 6Author Website:Countries AvailableAll regions |
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